AI and Tunnel Boring Machine

Academic Istitutions are researching how to use technology to better assist tunnel boring machines’ operations.

The studies show that an operator can’t control more than three or four of the more than two dozen parameters TBMs generate, to include real-time information about surface and subsurface settlement, vibrations, cutter-head speed and rotation and screw-conveyor activity. The Colorado School of Mines’ and other industry members are working to improve automation, including deploying artificial intelligence that will recognize data patterns from operator-generated inputs and performance-related outputs.

Through thousands of sensors placed throughout the TBM and on ground surfaces and other targets, researchers gather data so they can analyze the information using Colorado School of Mines-created algorithms to detect patterns. If those patterns can be identified early, researchers posit that tunnelers can use it to improve performance.

The TBM market is expected to grow at a compound annual growth rate of 4,2% by 2022. Because of the use of these type of machines continues to grow, it’s likely advancements and technologies such as Colorado School of Mines’ will grow alongside it.

Galbiati Groupbuilds TBM components such as planetary gearboxes, bearing supports, erectors. The company has recently realized 4 ton-segment erector for a Tunnel Boring Machine designed for a excavation project. For more information visit the website www.galbiatigroup.it.

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